Statistics time chart interface cell mode drill down

ABSTRACT

In embodiments of statistics time chart interface cell mode drill down, a first interface displays in a table format that includes columns each having a column heading comprising a different value, each different value associated with a particular event field, and includes one or more rows, each row having a time increment and aggregated metrics that each represent a number of events having a field-value pair that matches the different value represented in one of the columns and within the time increment over which the aggregated metric is calculated. A cell can be emphasized that includes one of the aggregated metrics in a row that includes the respective time increment, and in response, a menu displays options to transition to a second interface.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.14/526,478 filed Oct. 28, 2014 entitled “Statistics Time Chart InterfaceCell Mode Drill Down,” the disclosure of which is incorporated byreference herein in its entirety. The '478 application claims priorityto U.S. Provisional Patent Application Ser. Nos. 62/059,988 filed Oct.5, 2014; 62/059,989 filed Oct. 5, 2014; 62/059,993 filed Oct. 5, 2014;62/060,545 filed Oct. 6, 2014; 62/059,994 filed Oct. 5, 2014; 62/060,551filed Oct. 6, 2014; 62/059,998 filed Oct. 5, 2014; 62/060,560 filed Oct.6, 2014; 62/060,001 filed Oct. 5, 2014; and 62/060,567 filed Oct. 6,2014, the disclosures of which are incorporated by reference herein intheir entirety.

BACKGROUND

Data analysts for many businesses face the challenge of making sense ofand finding patterns in the increasingly large amounts of data in themany types and formats that such businesses generate and collect. Forexample, accessing computer networks and transmitting electroniccommunications across the networks generates massive amounts of data,including such types of data as machine data and Web logs. Identifyingpatterns in this data, once thought relatively useless, has proven to beof great value to the businesses. In some instances, pattern analysiscan indicate which patterns are normal and which ones are unusual. Forexample, detecting unusual patterns can allow a computer system managerto investigate the circumstances and determine whether a computer systemsecurity threat exists.

Additionally, analysis of the data allows businesses to understand howtheir employees, potential consumers, and/or Web visitors use thecompany's online resources. Such analysis can provide businesses withoperational intelligence, business intelligence, and an ability tobetter manage their IT resources. For instance, such analysis may enablea business to better retain customers, meet customer needs, or improvethe efficiency of the company's IT resources. Despite the value that onecan derive from the underlying data described, making sense of this datato realize that value takes effort. In particular, patterns inunderlying data may be difficult to identify or understand whenanalyzing specific behaviors in isolation, often resulting in thefailure of a data analyst to notice valuable correlations in the datafrom which a business can draw strategic insight.

SUMMARY

This Summary introduces features and concepts of statistics time chartinterface cell mode drill down, which is further described below in theDetailed Description and/or shown in the Figures. This Summary shouldnot be considered to describe essential features of the claimed subjectmatter, nor used to determine or limit the scope of the claimed subjectmatter.

Statistics time chart interface cell mode drill down, a search systemexposes a statistics value chart interface for display that includescolumns each having a column heading comprising a different value, eachdifferent value associated with a particular event field, and includesone or more rows, each row having a time increment and aggregatedmetrics that each represent a number of events having a field-value pairthat matches the different value represented in one of the columns andwithin the time increment over which the aggregated metric iscalculated. A cell can be emphasized that includes one of the aggregatedmetrics in a row that includes the respective time increment, and inresponse, a stats event menu is displayed with event options that areselectable. The stats event menu includes the options to transition to asecond interface based on a selected one of the options.

In embodiments, a selection of an emphasized cell initiates the displayof the menu with the options that include a view events option and anexclude from results option. An input associated with the emphasizedcell can be received, such as when initiated by a user in the statisticstime chart interface, and the menu of the options is displayed proximatethe emphasized cell in the statistics time chart interface. For example,the menu may pop-up or drop-down just below the emphasized cell. Theview events option is selectable to transition to the events interfacethat displays a narrowed list of the events that include the field-valuepair that matches the different value represented in one of the columnsand within a time duration of the time increment in the row with theemphasized cell. The exclude from results option is selectable to drilldown into the statistics time chart interface excluding the field-valuepair corresponding to the column of the respective value. Inembodiments, the menu includes the view events option and acorresponding designation of a time duration that encompasses the timeincrement corresponding to the emphasized cell. The designation furtherdisplays the field-value pair corresponding to the column of therespective value. The menu also includes the exclude from results optionand a corresponding designation displaying the field-value paircorresponding to the column of the respective value.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of statistics time chart interface cell mode drill down aredescribed with reference to the following Figures. The same numbers maybe used throughout to reference like features and components that areshown in the Figures:

FIG. 1 illustrates a block diagram of an event-processing system inaccordance with the disclosed implementations of statistics time chartinterface cell mode drill down.

FIG. 2 illustrates a flowchart of how indexers process, index, and storedata received from forwarders in accordance with the disclosedimplementations.

FIG. 3 illustrates a flowchart of how a search head and indexers performa search query in accordance with the disclosed implementations.

FIG. 4 illustrates a block diagram of a system for processing searchrequests that uses extraction rules for field values in accordance withthe disclosed implementations.

FIG. 5 illustrates an exemplary search query received from a client andexecuted by search peers in accordance with the disclosedimplementations.

FIG. 6A illustrates a search screen in accordance with the disclosedimplementations.

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed implementations.

FIG. 7A illustrates a key indicators view in accordance with thedisclosed implementations.

FIG. 7B illustrates an incident review dashboard in accordance with thedisclosed implementations.

FIG. 7C illustrates a proactive monitoring tree in accordance with thedisclosed implementations.

FIG. 7D illustrates a screen displaying both log data and performancedata in accordance with the disclosed implementations.

FIGS. 8A-8E illustrate examples of statistics search interfaces in cellmode in accordance with the disclosed implementations.

FIG. 9A-9D illustrate examples of statistics search interfaces in cellmode in accordance with the disclosed implementations.

FIG. 10 illustrates example method(s) of statistics value chartinterface cell mode drill down in accordance with one or moreembodiments.

FIGS. 11 illustrates example method(s) of statistics time chartinterface cell mode drill down in accordance with one or moreembodiments.

FIG. 12 illustrates an example system with an example device that canimplement embodiments of statistics time chart interface cell mode drilldown.

DETAILED DESCRIPTION

Embodiments of statistics interface cell mode search drill down aredescribed and can be implemented to facilitate user-initiated searchoptions when performing data searches in statistics value chartinterfaces and statistics time chart interfaces. A statistics valuechart interface includes columns each with field values of an eventfield, and each column having a column heading of a different one of theevent fields, and includes rows each with one or more of the fieldvalues, each field value in a row associated with a different one of theevent fields, and having an aggregated metric that represents a numberof events with field-value pairs that match all of the field valueslisted in a respective row and the corresponding event fields listed inthe respective columns. A cell can be emphasized that includes one ofthe field values in a row that corresponds to one of the different eventfields in a column, and in response, a menu is displayed with optionsthat are selectable. The menu includes the options to transition tosecond interface based on a selected one of the options.

Additionally, a statistics time chart interface includes columns eachhaving a column heading comprising a different value, each differentvalue associated with a particular event field, and includes one or morerows, each row having a time increment and aggregated metrics that eachrepresent a number of events having a field-value pair that matches thedifferent value represented in one of the columns and within the timeincrement over which the aggregated metric is calculated. A cell can beemphasized that includes one of the aggregated metrics in a row thatincludes the respective time increment, and in response, a stats eventmenu is displayed with event options that are selectable. The statsevent menu includes the options to transition to a second interfacebased on a selected one of the options.

Example Environment

Modern data centers often comprise thousands of host computer systemsthat operate collectively to service requests from even larger numbersof remote clients. During operation, these data centers generatesignificant volumes of performance data and diagnostic information thatcan be analyzed to quickly diagnose performance problems. In order toreduce the size of this performance data, the data is typicallypre-processed prior to being stored based on anticipated data-analysisneeds. For example, pre-specified data items can be extracted from theperformance data and stored in a database to facilitate efficientretrieval and analysis at search time. However, the rest of theperformance data is not saved and is essentially discarded duringpre-processing. As storage capacity becomes progressively cheaper andmore plentiful, there are fewer incentives to discard this performancedata and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed performance data at “ingestiontime” for later retrieval and analysis at “search time.” Note thatperforming the analysis operations at search time provides greaterflexibility because it enables an analyst to search all of theperformance data, instead of searching pre-specified data items thatwere stored at ingestion time. This enables the analyst to investigatedifferent aspects of the performance data instead of being confined tothe pre-specified set of data items that were selected at ingestiontime.

However, analyzing massive quantities of heterogeneous performance dataat search time can be a challenging task. A data center may generateheterogeneous performance data from thousands of different components,which can collectively generate tremendous volumes of performance datathat can be time-consuming to analyze. For example, this performancedata can include data from system logs, network packet data, sensordata, and data generated by various applications. Also, the unstructurednature of much of this performance data can pose additional challengesbecause of the difficulty of applying semantic meaning to unstructureddata, and the difficulty of indexing and querying unstructured datausing traditional database systems.

These challenges can be addressed by using an event-based system, suchas the SPLUNK® ENTERPRISE system produced by Splunk Inc. of SanFrancisco, California, to store and process performance data. TheSPLUNK® ENTERPRISE system is the leading platform for providingreal-time operational intelligence that enables organizations tocollect, index, and harness machine-generated data from variouswebsites, applications, servers, networks, and mobile devices that powertheir businesses. The SPLUNK® ENTERPRISE system is particularly usefulfor analyzing unstructured performance data, which is commonly found insystem log files. Although many of the techniques described herein areexplained with reference to the SPLUNK® ENTERPRISE system, thetechniques are also applicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as“events,” in which each event comprises a collection of performance dataand/or diagnostic information that is generated by a computer system andis correlated with a specific point in time. Events can be derived from“time series data,” in which time series data includes a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time and are typically spacedat uniform time intervals. Events can also be derived from “structured”or “unstructured” data. Structured data has a predefined format, inwhich specific data items with specific data formats reside atpredefined locations in the data. For example, structured data caninclude data items stored in fields in a database table. In contrast,unstructured data does not have a predefined format. This means thatunstructured data can include various data items having different datatypes that can reside at different locations. For example, when the datasource is an operating system log, an event can include one or morelines from the operating system log containing raw data that includesdifferent types of performance and diagnostic information associatedwith a specific point in time.

Examples of data sources from which an event may be derived include, butare not limited to web servers, application servers, databases,firewalls, routers, operating systems, and software applications thatexecute on computer systems, mobile devices, and sensors. The datagenerated by such data sources can be produced in various formsincluding, for example and without limitation, server log files,activity log files, configuration files, messages, network packet data,performance measurements and sensor measurements. An event typicallyincludes a timestamp that may be derived from the raw data in the event,or may be determined through interpolation between temporally proximateevents having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schemato specify how to extract information from the event data, in which theflexible schema may be developed and redefined as needed. Note that aflexible schema may be applied to event data “on the fly” as desired(e.g., at search time), rather than at ingestion time of the data as intraditional database systems. Because the schema is not applied to eventdata until it is desired (e.g., at search time), it is referred to as a“late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data,which can include unstructured data, machine data, performancemeasurements or other time-series data, such as data obtained fromweblogs, syslogs, or sensor readings. It divides this raw data into“portions,” and optionally transforms the data to produce timestampedevents. The system stores the timestamped events in a data store, andenables a user to run queries against the data store to retrieve eventsthat meet specified criteria, such as containing certain keywords orhaving specific values in defined fields. Note that the term “field”refers to a location in the event data containing a value for a specificdata item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using alate-binding schema while performing queries on events. A late-bindingschema specifies “extraction rules” that are applied to data in theevents to extract values for specific fields. More specifically, theextraction rules for a field can include one or more instructions thatspecify how to extract a value for the field from the event data. Anextraction rule can generally include any type of instruction forextracting values from data in events. In some cases, an extraction ruleincludes a regular expression, in which case the rule is referred to asa “regex rule.”

In contrast to a conventional schema for a database system, alate-binding schema is not defined at data ingestion time. Instead, thelate-binding schema can be developed on an ongoing basis until the timea query is actually executed. This means that extraction rules for thefields in a query may be provided in the query itself, or may be locatedduring execution of the query. Hence, as an analyst learns more aboutthe data in the events, the analyst can continue to refine thelate-binding schema by adding new fields, deleting fields, or changingthe field extraction rules until the next time the schema is used by aquery. Because the SPLUNK® ENTERPRISE system maintains the underlyingraw data and provides a late-binding schema for searching the raw data,it enables an analyst to investigate questions that arise as the analystlearns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain fields in the eventswhen the events are being created, indexed, or stored, or possibly at alater time. Alternatively, a user may manually define extraction rulesfor fields using a variety of techniques. Also, a number of “defaultfields” that specify metadata about the events, rather than data in theevents themselves, can be created automatically. For example, suchdefault fields can specify: a timestamp for the event data; a host fromwhich the event data originated; a source of the event data; and asource type for the event data. These default fields may be determinedautomatically when the events are created, indexed, or stored.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by different data sources, thesystem facilitates use of a “common information model” (CIM) across thedifferent data sources.

Data Server System

FIG. 1 illustrates a block diagram of an example event-processing system100, similar to the SPLUNK® ENTERPRISE system, and in which embodimentsof statistics time chart interface cell mode drill down can beimplemented. The example event-processing system 100 includes one ormore forwarders 101 that collect data obtained from a variety ofdifferent data sources 105, and one or more indexers 102 that store,process, and/or perform operations on this data, in which each indexeroperates on data contained in a specific data store 103. A search head104 may also be provided that represents functionality to obtain andprocess search requests from clients and provide results of the searchback to the clients, additional details of which are discussed inrelation to FIGS. 3 and 4. The forwarders 101, indexers 102, and/orsearch head 104 may be configured as separate computer systems in a datacenter, or alternatively may be configured as separate processesimplemented via one or more individual computer systems. Data that iscollected via the forwarders 101 may be obtained from a variety ofdifferent data sources 105.

As further illustrated, the search head 104 may interact with a clientapplication module 106 associated with a client device, such as toobtain search queries and supply search results or other suitable databack to the client application module 106 that is effective to enablethe client application module 106 to form search user interfaces 108through which different views of the data may be exposed. Variousexamples and details regarding search interfaces 108, client applicationmodules 106, search queries, and operation of the various componentsillustrated in FIG. 1 are discussed throughout this document.

During operation, the forwarders 101 identify which indexers 102 willreceive the collected data and then forward the data to the identifiedindexers. The forwarders 101 can also perform operations to strip outextraneous data and detect timestamps in the data. The forwarders nextdetermine which of the indexers 102 will receive each data item and thenforward the data items to the determined indexers 102. Note thatdistributing data across the different indexers 102 facilitates parallelprocessing. This parallel processing can take place at data ingestiontime, because multiple indexers can process the incoming data inparallel. The parallel processing can also take place at search time,because multiple indexers can search through the data in parallel.

The example event-processing system 100 and the processes describedbelow with respect to FIGS. 1-5 are further described in “ExploringSplunk Search Processing Language (SPL) Primer and Cookbook” by DavidCarasso, CITO Research, 2012, and in “Optimizing Data Analysis With aSemi-Structured Time Series Database” by Ledion Bitincka, ArchanaGanapathi, Stephen Sorkin, and Steve Zhang, SLAML, 2010, each of whichis hereby incorporated herein by reference in its entirety for allpurposes.

Data Ingestion

FIG. 2 illustrates a flowchart 200 of how an indexer processes, indexes,and stores data received from forwarders in accordance with thedisclosed embodiments. At block 201, the indexer receives the data fromthe forwarder. Next, at block 202, the indexer apportions the data intoevents. Note that the data can include lines of text that are separatedby carriage returns or line breaks and an event may include one or moreof these lines. During the apportioning process, the indexer can useheuristic rules to automatically determine the boundaries of the events,which for example coincide with line boundaries. These heuristic rulesmay be determined based on the source of the data, in which the indexercan be explicitly informed about the source of the data or can infer thesource of the data by examining the data. These heuristic rules caninclude regular expression-based rules or delimiter-based rules fordetermining event boundaries, in which the event boundaries may beindicated by predefined characters or character strings. Thesepredefined characters may include punctuation marks or other specialcharacters including, for example, carriage returns, tabs, spaces orline breaks. In some cases, a user can fine-tune or configure the rulesthat the indexers use to determine event boundaries in order to adaptthe rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. Asmentioned above, these timestamps can be determined by extracting thetime directly from data in the event, or by interpolating the time basedon timestamps from temporally proximate events. In some cases, atimestamp can be determined based on the time the data was received orgenerated. The indexer subsequently associates the determined timestampwith each event at block 204, for example by storing the timestamp asmetadata for each event.

Then, the system can apply transformations to data to be included inevents at block 205. For log data, such transformations can includeremoving a portion of an event (e.g., a portion used to define eventboundaries, extraneous text, characters, etc.) or removing redundantportions of an event. Note that a user can specify portions to beremoved using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fastkeyword searching for events. To build a keyword index, the indexerfirst identifies a set of keywords in block 206. Then, at block 207 theindexer includes the identified keywords in an index, which associateseach stored keyword with references to events containing that keyword(or to locations within events where that keyword is located). When anindexer subsequently receives a keyword-based query, the indexer canaccess the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, wherein a name-value pair can includea pair of keywords connected by a symbol, such as an equals sign orcolon. In this way, events containing these name-value pairs can bequickly located. In some embodiments, fields can automatically begenerated for some or all of the name-value pairs at the time ofindexing. For example, if the string “dest=10.0.1.2” is found in anevent, a field named “dest” may be created for the event, and assigned avalue of “10.0.1.2” as a field-value pair.

Finally, the indexer stores the events in a data store at block 208,where a timestamp can be stored with each event to facilitate searchingfor events based on a time range. In some cases, the stored events areorganized into a plurality of buckets, where each bucket stores eventsassociated with a specific time range. This not only improves time-basedsearches, but it also allows events with recent timestamps that may havea higher likelihood of being accessed to be stored in faster memory tofacilitate faster retrieval. For example, a bucket containing the mostrecent events can be stored as flash memory instead of on a hard disk.

Each indexer 102 is responsible for storing and searching a subset ofthe events contained in a corresponding data store 103. By distributingevents among the indexers and data stores, the indexers can analyzeevents for a query in parallel, for example using map-reduce techniques,in which each indexer returns partial responses for a subset of eventsto a search head that combines the results to produce an answer for thequery. By storing events in buckets for specific time ranges, an indexermay further optimize searching by looking only in buckets for timeranges that are relevant to a query. Moreover, events and buckets canalso be replicated across different indexers and data stores tofacilitate high availability and disaster recovery as is described inU.S. patent application Ser. No. 14/266,812 filed on 30 Apr. 2014, andin U.S. application patent Ser. No. 14/266,817 also filed on 30 Apr.2014.

Query Processing

FIG. 3 illustrates a flowchart 300 of how a search head and indexersperform a search query in accordance with the disclosed embodiments. Atthe start of this process, a search head receives a search query from aclient (e.g., a client computing device) at block 301. Next, at block302, the search head analyzes the search query to determine whatportions can be delegated to indexers and what portions need to beexecuted locally by the search head. At block 303, the search headdistributes the determined portions of the query to the indexers. Notethat commands that operate on single events can be trivially delegatedto the indexers, while commands that involve events from multipleindexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributedsearch their data stores for events that are responsive to the query. Todetermine which events are responsive to the query, the indexer searchesfor events that match the criteria specified in the query. This criteriacan include matching keywords or specific values for certain fields. Ina query that uses a late-binding schema, the searching operations inblock 304 may involve using the late-binding scheme to extract valuesfor specified fields from events at the time the query is processed.Next, the indexers can either send the relevant events back to thesearch head, or use the events to calculate a partial result, and sendthe partial result back to the search head.

Finally, at block 305, the search head combines the partial resultsand/or events received from the indexers to produce a final result forthe query. This final result can comprise different types of datadepending on what the query is asking for. For example, the finalresults can include a listing of matching events returned by the query,or some type of visualization of data from the returned events. Inanother example, the final result can include one or more calculatedvalues derived from the matching events.

Moreover, the results generated by system 100 can be returned to aclient using different techniques. For example, one technique streamsresults back to a client in real-time as they are identified. Anothertechnique waits to report results to the client until a complete set ofresults is ready to return to the client. Yet another technique streamsinterim results back to the client in real-time until a complete set ofresults is ready, and then returns the complete set of results to theclient. In another technique, certain results are stored as “searchjobs,” and the client may subsequently retrieve the results byreferencing the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head starts executing aquery, the search head can determine a time range for the query and aset of common keywords that all matching events must include. Next, thesearch head can use these parameters to query the indexers to obtain asuperset of the eventual results. Then, during a filtering stage, thesearch head can perform field-extraction operations on the superset toproduce a reduced set of search results.

Field Extraction

FIG. 4 illustrates a block diagram 400 of how fields can be extractedduring query processing in accordance with the disclosed embodiments. Atthe start of this process, a search query 402 is received at a queryprocessor 404. The query processor 404 includes various mechanisms forprocessing a query, where these mechanisms can reside in a search head104 and/or an indexer 102. Note that the exemplary search query 402illustrated in FIG. 4 is expressed in Search Processing Language (SPL),which is used in conjunction with the SPLUNK® ENTERPRISE system. The SPLis a pipelined search language in which a set of inputs is operated onby a first command in a command line, and then a subsequent commandfollowing the pipe symbol “I” operates on the results produced by thefirst command, and so on for additional commands. Search query 402 canalso be expressed in other query languages, such as the Structured QueryLanguage (“SQL”) or any suitable query language.

Upon receiving the search query 402, the query processor 404 identifiesthat the search query 402 includes two fields, “IP” and “target.” Thequery processor 404 also determines that the values for the “IP” and“target” fields have not already been extracted from events in a datastore 414, and consequently determines that the query processor 404needs to use extraction rules to extract values for the fields. Hence,the query processor 404 performs a lookup for the extraction rules in arule base 406, in which rule base 406 maps field names to correspondingextraction rules and obtains extraction rules 408 and 409, whereextraction rule 408 specifies how to extract a value for the “IP” fieldfrom an event, and extraction rule 409 specifies how to extract a valuefor the “target” field from an event.

As is illustrated in FIG. 4, the extraction rules 408 and 409 caninclude regular expressions that specify how to extract values for therelevant fields. Such regular-expression-based extraction rules are alsoreferred to as “regex rules.” In addition to specifying how to extractfield values, the extraction rules may also include instructions forderiving a field value by performing a function on a character string orvalue retrieved by the extraction rule. For example, a transformationrule may truncate a character string, or convert the character stringinto a different data format. In some cases, the query itself canspecify one or more extraction rules.

Next, the query processor 404 sends the extraction rules 408 and 409 toa field extractor 412, which applies the extraction rules 408 and 409 toevents 416-418 in the data store 414. Note that the data store 414 caninclude one or more data stores, and the extraction rules 408 and 409can be applied to large numbers of events in the data store 414, and arenot meant to be limited to the three events 416-418 illustrated in FIG.4. Moreover, the query processor 404 can instruct the field extractor412 to apply the extraction rules to all of the events in the data store414, or to a subset of the events that have been filtered based on somecriteria.

Next, the field extractor 412 applies the extraction rule 408 for thefirst command “Search IP=“10*” to events in the data store 414,including the events 416-418. The extraction rule 408 is used to extractvalues for the IP address field from events in the data store 414 bylooking for a pattern of one or more digits, followed by a period,followed again by one or more digits, followed by another period,followed again by one or more digits, followed by another period, andfollowed again by one or more digits. Next, the field extractor 412returns field values 420 to the query processor 404, which uses thecriterion IP=″10*” to look for IP addresses that start with “10”. Notethat events 416 and 417 match this criterion, but event 418 does not, sothe result set for the first command is events 416 and 417.

The query processor 404 then sends the events 416 and 417 to the nextcommand “stats count target.” To process this command, the queryprocessor 404 causes the field extractor 412 to apply the extractionrule 409 to the events 416 and 417. The extraction rule 409 is used toextract values for the target field for the events 416 and 417 byskipping the first four commas in the events, and then extracting all ofthe following characters until a comma or period is reached. Next, thefield extractor 412 returns field values 421 to the query processor 404,which executes the command “stats count target” to count the number ofunique values contained in the target fields, which in this exampleproduces the value “2” that is returned as a final result 422 for thequery.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, the queryresults may include: a set of one or more events; a set of one or morevalues obtained from the events; a subset of the values; statisticscalculated based on the values; a report containing the values; or avisualization, such as a graph or chart, generated from the values.

Example Search Screen

FIG. 6A illustrates an example of a search screen 600 in accordance withthe disclosed embodiments. The search screen 600 includes a search bar602 that accepts user input in the form of a search string. It alsoincludes a date time range picker 612 that enables the user to specify adate and/or time range for the search. For “historical searches” theuser can select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday,” or “last week.” For “real-timesearches,” the user can select the size of a preceding time window tosearch for real-time events. The search screen 600 also initiallydisplays a “data summary” dialog 610 as is illustrated in FIG. 6B thatenables the user to select different sources for the event data, such asby selecting specific hosts and log files.

After the search is executed, the search screen 600 can display theresults through search results tabs 604, where the search results tabs604 include: an “Events” tab that displays various information aboutevents returned by the search; a “Patterns” tab that can be selected todisplay various patterns about the events returned by the search; a“Statistics” tab that displays statistics about the search results andevents; and a “Visualization” tab that displays various visualizationsof the search results. The “Events” tab illustrated in FIG. 6A displaysa timeline graph 605 that graphically illustrates the number of eventsthat occurred in one-hour intervals over the selected time range. Italso displays an events list 608 that enables a user to view the rawdata in each of the returned events. It additionally displays a fieldssidebar 606 that includes statistics about occurrences of specificfields in the returned events, including “selected fields” that arepre-selected by the user, and “interesting fields” that areautomatically selected by the system based on pre-specified criteria.

Acceleration Techniques

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed performancedata “on the fly” at search time instead of storing pre-specifiedportions of the performance data in a database at ingestion time. Thisflexibility enables a user to see correlations in the performance dataand perform subsequent queries to examine interesting aspects of theperformance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause considerable delays whileprocessing the queries. Fortunately, a number of acceleration techniqueshave been developed to speed up analysis operations performed at searchtime. These techniques include: (1) performing search operations inparallel by formulating a search as a map-reduce computation; (2) usinga keyword index; (3) using a high performance analytics store; and (4)accelerating the process of generating reports. These techniques aredescribed in more detail below.

Map-Reduce Technique

To facilitate faster query processing, a query can be structured as amap-reduce computation, wherein the “map” operations are delegated tothe indexers, while the corresponding “reduce” operations are performedlocally at the search head. For example, FIG. 5 illustrates an example500 of how a search query 501 received from a client at search head 104can split into two phases, including: (1) a “map phase” comprisingsubtasks 502 (e.g., data retrieval or simple filtering) that may beperformed in parallel and are “mapped” to indexers 102 for execution,and (2) a “reduce phase” comprising a merging operation 503 to beexecuted by the search head 104 when the results are ultimatelycollected from the indexers.

During operation, upon receiving search query 501, search head 104modifies search query 501 by substituting “stats” with “prestats” toproduce search query 502, and then distributes search query 502 to oneor more distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as is illustrated in FIG. 3, or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head performs the merging operations 503 on theresults. Note that by executing the computation in this way, the systemeffectively distributes the computational operations while minimizingdata transfers.

Keyword Index

As described above with reference to the flow charts 200 and 300 shownin respective FIGS. 2 and 3, the event-processing system 100 canconstruct and maintain one or more keyword indices to facilitate rapidlyidentifying events containing specific keywords. This can greatly speedup the processing of queries involving specific keywords. As mentionedabove, to build a keyword index, an indexer first identifies a set ofkeywords. Then, the indexer includes the identified keywords in anindex, which associates each stored keyword with references to eventscontaining that keyword, or to locations within events where thatkeyword is located. When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword.

High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100make use of a high-performance analytics store, which is referred to asa “summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an entry in a summarization table can keep track of occurrences of thevalue “94107” in a “ZIP code” field of a set of events, where the entryincludes references to all of the events that contain the value “94107”in the ZIP code field. This enables the system to quickly processqueries that seek to determine how many events have a particular valuefor a particular field, because the system can examine the entry in thesummarization table to count instances of the specific value in thefield without having to go through the individual events or doextractions at search time. Also, if the system needs to process each ofthe events that have a specific field-value combination, the system canuse the references in the summarization table entry to directly accessthe events to extract further information without having to search eachof the events to find the specific field-value combination at searchtime.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range, where a bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer, in which theindexer-specific summarization table only includes entries for theevents in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query”that scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A collection query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Acollection query can also be automatically launched in response to aquery that asks for a specific field-value combination.

In some cases, the summarization tables may not cover each of the eventsthat are relevant to a query. In this case, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query.This summarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. This is possible if results from preceding timeperiods can be computed separately and combined to generate an updatedreport. In some cases, it is not possible to combine such incrementalresults, for example where a value in the report depends onrelationships between events from different time periods. If reports canbe accelerated, the summarization engine periodically generates asummary covering data obtained during a latest non-overlapping timeperiod. For example, where the query seeks events meeting a specifiedcriteria, a summary for the time period includes only the events withinthe time period that meet the specified criteria. Similarly, if thequery seeks statistics calculated from the events, such as the number ofevents that match the specified criteria, then the summary for the timeperiod includes the number of events in the period that match thespecified criteria.

In parallel with the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated.

Alternatively, if the system stores events in buckets covering specifictime ranges, then the summaries can be generated on a bucket-by-bucketbasis. Note that producing intermediate summaries can save the workinvolved in re-running the query for previous time periods, so only thenewer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, issued on Nov. 19, 2013, and in U.S.Pat. No. 8,412,696, issued on Apr. 2, 2011.

Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that make it easy for developers to createapplications to provide additional capabilities. One such application isthe SPLUNK® APP FOR ENTERPRISE SECURITY, which performs monitoring andalerting operations, and includes analytics to facilitate identifyingboth known and unknown security threats based on large volumes of datastored by the SPLUNK® ENTERPRISE system. This differs significantly fromconventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related event data. Traditional SIEM systemstypically use fixed schemas to extract data from pre-definedsecurity-related fields at data ingestion time, where the extracted datais typically stored in a relational database. This data extractionprocess (and associated reduction in data size) that occurs at dataingestion time inevitably hampers future incident investigations, whenall of the original data may be needed to determine the root cause of asecurity issue, or to detect the tiny fingerprints of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. patentapplication Ser. Nos. 13/956,252, and 13/956,262. Security-relatedinformation can also include endpoint information, such as malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices, and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting so-called “notable events” that are likely to indicate asecurity threat. These notable events can be detected in a number ofways: (1) an analyst can notice a correlation in the data and canmanually identify a corresponding group of one or more events as“notable;” or (2) an analyst can define a “correlation search”specifying criteria for a notable event, and every time one or moreevents satisfy the criteria, the application can indicate that the oneor more events are notable. An analyst can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics of interest, such as countsof different types of notable events. For example, FIG. 7A illustratesan exemplary key indicators view 700 that comprises a dashboard, whichcan display a value 701, for various security-related metrics, such asmalware infections 702. It can also display a change in a metric value703, which indicates that the number of malware infections increased bysixty-three (63) during the preceding interval. The key indicators view700 additionally displays a histogram panel 704 that displays ahistogram of notable events organized by urgency values, and a histogrampanel 705 of notable events organized by time intervals. This keyindicators view is described in further detail in pending U.S. patentapplication Ser. No. 13/956,338 filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 7B illustrates an example of an incident review dashboard 710 thatincludes a set of incident attribute fields 711 that, for example,enables a user to specify a time range field 712 for the displayedevents. It also includes a timeline 713 that graphically illustrates thenumber of incidents that occurred in one-hour time intervals over theselected time range. It additionally displays an events list 714 thatenables a user to view a list of each of the notable events that matchthe criteria in the incident attributes fields 711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, or critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that make it easy for developers to create variousapplications. One such application is the SPLUNK® APP FOR VMWARE®, whichperforms monitoring operations and includes analytics to facilitatediagnosing the root cause of performance problems in a data center basedon large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lackthe infrastructure to effectively store and analyze large volumes ofperformance information and log data obtained from the data center. Inconventional data-center-monitoring systems, this performance data istypically pre-processed prior to being stored, for example by extractingpre-specified data items from the performance data and storing them in adatabase to facilitate subsequent retrieval and analysis at search time.However, the rest of the performance data is not saved and isessentially discarded during pre-processing. In contrast, the SPLUNK®APP FOR VMWARE® stores large volumes of minimally processed performanceinformation and log data at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated.

The SPLUNK® APP FOR VMWARE® can process many types ofperformance-related information. In general, this performance-relatedinformation can include any type of performance-related data and logdata produced by virtual machines and host computer systems in a datacenter. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. For moredetails about such performance metrics, please see U.S. patent Ser. No.14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein byreference.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas. The SPLUNK® APP FOR VMWARE® additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Exemplary node-expansionoperations are illustrated in FIG. 7C, where nodes 733 and 734 areselectively expanded. Note that the nodes 731-739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state, or anunknown/off-line state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. patent application Ser. No. 14/235,490 filed on15 Apr. 2014, which is hereby incorporated herein by reference for allpossible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous data,comprising events, log data, and associated performance metrics, for theselected time range. For example, the interface screen illustrated inFIG. 7D displays a listing of recent “tasks and events” and a listing ofrecent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316 filed on 29 Jan. 2014, which is hereby incorporatedherein by reference for all possible purposes.

Statistics Value Chart Interface Cell Mode Drill Down

FIG. 8A illustrates an example of a statistics value chart interface 800displayed as a graphical user interface in accordance with the disclosedembodiments for statistics value chart interface cell mode drill down.The statistics value chart interface 800 includes a search bar 802 thatdisplays a search command 804. The statistics value chart interface 800displays columns 806 of field values 808 for designated event fields810. The statistics value chart interface 800 also includes rows 812that each include the field values 808 of the respective event fields810 in a particular row. For example, the first row 812 in the interfaceincludes the field value “/Users/cburke/ . . ./log/splunk/web_service.log” of the event field “source”; includes thefield value “splunk_web_service” of the event field “sourcetype”; andincludes the field value “cached” of the event field “component”.

The statistics value chart interface 800 also includes aggregatedmetrics 814 that each identify the number of events having the fieldvalues 808 of the event fields 810 that are listed in a respective row812. For example, the first row 812 of the statistics value chartinterface 800 has an aggregated metric of “1”, indicating that one eventincludes the field-value pairs for “source_type=splunk_web service”,“source=/Users/cburke/ . . . /log/splunk/web_service.log”, and“component=cached”. In implementations, the aggregated metrics 814 maybe any type of metric, such as a count, an average, a sum, or any otheraggregating metric associated with a search result set of events.

In implementations, a cell 816 in a row 812 of the statistics valuechart interface 800 may be emphasized (e.g., highlighted or any othertype of visual emphasis) when a pointer that is displayed moves over aparticular cell. This feature is also referred to as highlight withrollover (e.g., detected when a pointer moves over a cell). For example,a user may move a computer mouse, stylus, or other input device pointerover the cell 816, which is then displayed as an emphasized cell. Theemphasized cell can then be selected in response to a user input, suchas with a mouse click or touch input to select a particular cell, suchas shown and described with reference to FIG. 8B.

In FIG. 8A, the statistics value chart interface 800 can be displayed ina table format that includes one or more columns, each column comprisingfield values of an event field, and each column having a column headingcomprising a different one of the event fields. The statistics valuechart interface 800 also includes one or more rows, each row comprisingone or more of the field values, each field value in a row associatedwith a different one of the event fields, and each row comprising anaggregated metric that represents a number of events having field-valuepairs that match all of the one or more field values listed in arespective row and the corresponding event fields listed in therespective columns. A cell in the table format can be emphasized, thecell including one of the field values in a row that corresponds to oneof the different event fields in a column, and in response, a menu isdisplayed with options that are selectable to transition to a secondinterface based on a selected one of the options. In embodiments, anevents interface displays either a list of events that include thefield-value pairs that match the field value of the emphasized cell, orother events that do not include the field-value pairs that match thefield value of the emphasized cell. In other embodiments, the statisticsvalue chart interface is displayed excluding the field value in theemphasized cell and the corresponding column.

FIG. 8B further illustrates the example of the statistics value chartinterface 800 described with reference to FIG. 8A in accordance with thedisclosed embodiments for statistics value chart interface cell modedrill down. In this example display of the statistics value chartinterface 800, the cell 816 is emphasized and a user has selected theemphasized cell, such as with a mouse click or touch input, whichinitiates a display of a stats event menu 818 that is displayedresponsive to the user input. In implementations, the stats event menu818 is displayed proximate the emphasized cell 816 in the statisticsvalue chart interface 800, such as a pop-up or drop-down menu just belowthe emphasized cell.

The stats event menu 818 includes event options 820 that are selectableto transition to an events interface that is shown and further describedwith reference to FIG. 8C. A user can select an event option 820 fromthe stats event menu 818 to drill down into events that match the tokenin an emphasized cell, where the “token” is a field value. The eventsinterface can display a list of the events that include the field value808 (e.g., “splunk_web_service” in this example) that corresponds to thecolumn with the emphasized cell 816. Alternatively, the events interfacecan display other events that do not include the field value 808 listedin the emphasized cell.

For example, the event options 820 displayed in the stats event menu 818include an option “View events” 822 that a user can select to transitionto the events interface (FIG. 8C) that displays a list of the eventsthat include the field value 808 that corresponds to the column with theemphasized cell 816. The event options 820 displayed in the stats eventmenu 818 also include an option “Other events” 824 that a user canselect to transition to the events interface that displays a list ofother events that do not include the field value 808 that corresponds tothe column with the emphasized cell 816. The stats event menu 818 alsoincludes a designation 826 of a field-value pair that is associated withthe emphasized cell in the statistics value chart interface 800. Thefield-value pair displayed as the designation 826 indicates the searchdrill down relevance of the field-value pair, which is“sourcetype=splunk_web service” in this example.

The stats event menu 818 also includes the search options 820 that areselectable to operate on the field value 808 (e.g., “splunk_web_service”in this example) that corresponds to the column with the emphasized cell816. For example, the search options 820 displayed in the stats eventmenu 818 include an option “Exclude from results” 828 that a user canselect to drill down and exclude the field-value pair, which initiatesdisplaying the statistics value chart interface 800 excluding the fieldvalue in the emphasized cell 816. The search options 820 displayed inthe stats event menu 818 also include an option “New search” 830 thatthe user can select to create a new search based on the field value inthe emphasized cell 816 (e.g., replacing the search command 804 in thesearch bar 802 with the field value in the emphasized cell). A userselection of the new search option 830 from the stats event menu 818 canbe received, and the search command 804 in the search bar 802 is updatedbased on the search option that is selected for the field value.

FIG. 8C illustrates an example of an events interface 832 displayed as agraphical user interface in accordance with the disclosedimplementations. The events interface 832 includes the search bar 802that displays a search command 804, which is“sourcetype=access_combined” in this example. The events interface 832also displays events 834 that are each correlated by a date and time836. As described previously, the events 834 are a result set ofperforming the search command 804 that is currently displayed in thesearch bar 802, and only a subset of the events are shown in the eventsinterface. A user can scroll through the list of events 834 in theevents interface 832 to view additional events of the search result setthat are not displayed.

An event 838 (e.g., the first displayed event in the list of events 834)generally incudes displayed event information, depending on a selectedevent view from which a user can select a format to display some or allof the event information for each of the events 834 in the eventsinterface. In the example events interface 832, the events 834 aredisplayed in a list view, in which case the displayed event informationfor event 838 includes event raw data 840 displayed in an upper portionof the event display area, and includes field-value pairs 842 displayedin a lower portion of the event display area. In this example, each ofthe events 834 include the current search command 804 (e.g.,“sourcetype=access_combined”) as a field-value pair 842. The eventsinterface 832 also includes a fields sidebar 844, which displays theselected fields 846 that are also displayed as the fields 842 for eachof the events 834, and the fields sidebar 844 includes other interestingfields 848.

FIG. 8D further illustrates the example of the statistics value chartinterface 800 described with reference to FIGS. 8A and 8B in accordancewith the disclosed embodiments for statistics value chart interface cellmode drill down. In this example display of the statistics value chartinterface 800, a cell 850 is emphasized and a user has selected theemphasized cell, such as with a mouse click or touch input, whichinitiates a display of the stats event menu 818 as described withreference to FIG. 8B. In this example, the stats event menu 818 reflectsa split-by search approach, and the designation 826, as well as anadditional designation 852, are updated to display the searchfield-value pairs added to the stats event menu 818 as cells of thestatistics value chart interface 800 are selected progressively movingto the right across the interface. The progressive nature of the exampleis further illustrated with reference to FIG. 8E.

The designation 826 updates to display the field-value pair with thefield value of the currently emphasized cell 850. In this example, thedesignation 826 corresponds to the event options 820 shown in the statsevent menu 818 as described with reference to FIG. 8B, and thefield-value pair displayed as the designation 826 is“source=/Users/cburke/ . . . /log/splunk/web_service.log” from theemphasized cell 850. In this example, the additional designation 852 isupdated to display the field value of the currently emphasized cell 850,as well as all of the field-value pairs to the left in the cells acrossthe statistics value chart interface 800 in the row 812 that correspondsto the emphasized cell 850. In this example, the designation 852corresponds to additional event options 854 that are user selectable andinclude an option “View events” 856 and an option “Other events” 858.The field-value pairs displayed in the designation 852 are“source=/Users/cburke/ . . . /log/splunk/web_service.log” from theemphasized cell 850, as well as “sourcetype=splunk_web_service” from thecell to the left in the row 812 that corresponds to the emphasized cell850 in the statistics value chart interface 800. Alternatively, theadditional designation 852 may include the field value of the currentlyemphasized cell 850, as well as any one or combination of thefield-value pairs in the cells across the statistics value chartinterface 800 in the row 812 that corresponds to the emphasized cell850.

The stats event menu 818 includes the additional event options 854 thatare selectable to transition to the events interface that is shown andfurther described with reference to FIG. 8C. A user can select an eventoption 854 from the stats event menu 818 to drill down into events thatmatch the token in an emphasized cell, where the “token” is a fieldvalue. The events interface can display a list of the events thatinclude the field values displayed in the corresponding designation 852.Alternatively, the events interface can display other events that do notinclude the field values listed in the corresponding designation.

For example, the event options 854 displayed in the stats event menu 818include the option “View events” 856 that a user can select totransition to the events interface (FIG. 8C) that displays a list of theevents that include the field values that correspond to the designation852. The event options 854 displayed in the stats event menu 818 alsoinclude the option “Other events” 858 that a user can select totransition to the events interface that displays a listing of otherevents that do not include the field values in the correspondingdesignation 852.

FIG. 8E further illustrates the example of the statistics value chartinterface 800 described with reference to FIGS. 8A, 8B, and 8D inaccordance with the disclosed embodiments for statistics value chartinterface cell mode drill down. In this example display of thestatistics value chart interface 800, a cell 860 is emphasized and auser has selected the emphasized cell, such as with a mouse click ortouch input, which initiates a display of the stats event menu 818 asdescribed with reference to FIGS. 8B and 8D. In this example, the statsevent menu 818 reflects a split-by search approach, and the designation826, as well as an additional designation 852, are updated to displaythe search field-value pairs added to the stats event menu 818 as cellsof the statistics value chart interface 800 are selected progressivelymoving to the right across the interface. The progressive nature of theexample is illustrated with reference to FIGS. 8B, 8D, and 8E.

The designation 826 updates to display the field-value pair with thefield value of the currently emphasized cell 860. In this example, thedesignation 826 corresponds to the event options 820 shown in the statsevent menu 818 as described with reference to FIG. 8B, and thefield-value pair displayed as the designation 826 is “component=utils”from the value listed in the emphasized cell 860. The additionaldesignation 852 is updated to display the field value of the currentlyemphasized cell 860, as well as all of the field-value pairs to the leftin the cells across the statistics value chart interface 800 in the row812 that corresponds to the emphasized cell 860. In this example, thedesignation 852 displays the field-value pairs “component=utils” fromthe value listed in the emphasized cell 860, as well as“source=/Users/cburke/ . . . /log/splunk/web_service.log” from the valuelisted in the cell to the left in the row 812, and“sourcetype=splunk_web_service” from the cell farther to the left in therow 812 in the statistics value chart interface 800.

Statistics Time Chart Interface Cell Mode Drill Down

FIG. 9A illustrates an example of a statistics time chart interface 900displayed as a graphical user interface in accordance with the disclosedembodiments for statistics time chart interface cell mode drill down.The statistics time chart interface 900 includes a search bar 902 thatdisplays a search command 904. The statistics time chart interface 900displays rows 906, where each row is designated by a time increment 908,and each time increment may include a date associated with the timeincrement. The statistics time chart interface 900 also includes columnsof field values 910 that are associated with an event field, such as thefield “sourcetype” in the search command 904 in this example. Each rowin the interface 900 includes a time increment 908 and one or moreaggregated metrics 912, where each aggregated metric represents a numberof events having the respective value 910 that is listed in thecorresponding column and within the time increment.

For example, a row 914 in the statistics time chart interface 900 has adate and time increment 916 of “2014-09-22 11:43:20”, and includes anaggregated metric “95” shown at 918, indicating that ninety-five eventshave the value 920 “splunkd” that is listed in the corresponding column922 and within the time increment 916 in row 914. For a given row andgiven column, the aggregated number is the count of the field-valuepairs that are within the designated time increment (also referred to asa “time bucket”). In implementations, the aggregated metrics 912 may beany type of metric, such as a count, an average, a sum, or any otheraggregating metric associated with a search result set of events.

In implementations, a cell 923 in the statistics time chart interface900 may be emphasized (e.g., highlighted or any other type of visualemphasis) when a pointer that is displayed moves over a particular cell.This feature is also referred to as highlight with rollover (e.g., whena pointer moves over a cell). For example, a user may move a computermouse, stylus, or other input device pointer over the cell 923, which isthen displayed as an emphasized cell. The emphasized cell can then beselected in response to a user input, such as with a mouse click ortouch input to select a particular cell, such as shown and describedwith reference to FIG. 9B.

In FIG. 9A, the statistics time chart interface 900 can be displayed ina table format that includes one or more columns, each column having acolumn heading comprising a different value, each different valueassociated with a particular event field. The statistics time chartinterface 900 also includes one or more rows, each row comprising a timeincrement and one or more aggregated metrics, each aggregated metricrepresenting a number of events having a field-value pair that matchesthe different value represented in one of the columns and within thetime increment over which the aggregated metric is calculated. A cell inthe table format can be emphasized, the cell including one of theaggregated metrics in a row that includes the respective time increment,and in response, a menu is displayed with options that are selectable totransition to a second interface based on a selected one of the options.In embodiments, the second interface is an events interface thatdisplays a narrowed list of events that include the field-value pairthat matches the different value represented in one of the columns andwithin a time duration of the time increment in the row with theemphasized cell. In other embodiments, the second interface is the firstinterface displayed excluding the field-value pair corresponding to thecolumn of the respective value.

FIG. 9B further illustrates the example of the statistics time chartinterface 900 described with reference to FIG. 9A in accordance with thedisclosed embodiments for statistics time chart interface cell modedrill down. In this example display of the statistics time chartinterface 900, a user has selected an emphasized cell 925 in the row914, such as with a mouse click or touch input, which initiates displayof a stats cell menu 924 that is displayed responsive to the user input.In implementations, the stats cell menu 924 is displayed proximate theemphasized cell 925 in the statistics time chart interface 900, such asa pop-up or drop-down menu just below the emphasized cell.

The stats cell menu 924 includes options 926 that are selectable totransition to an events interface that displays a narrowed list ofevents that correspond to the time increment 916 of the row 914 with theemphasized cell 925. For example, the options 926 displayed in the statscell menu 924 include an option “View events” 928 that a user can selectto transition to the events interface that displays the narrowed list ofthe events that include a field-value pair with the respective value 920that is listed in the corresponding column 922 and within a timeduration 930 of the time increment 916 of the row 914 with theemphasized cell 925. The stats cell menu 924 includes a designation 932that corresponds to the selectable option 928, the designation 932 beingassociated with the time duration 930 that encompasses the timeincrement 916 corresponding to the row 914 with the emphasized cell 925.For example, the time duration 930 is designated as “11:43:30 to11:43:40”, which encompasses the time increment 916 and is a windowed10-seconds of time. The designation 932 also displays the field-valuepair with the respective value 920 that is listed in the correspondingcolumn 922 (e.g., “sourcetype=splunkd” in this example).

The stats cell menu 924 also includes options 926 that are selectable todrill down into the table and initiate displaying the statistics timechart interface 900 excluding the particular value 920 that is listed inthe corresponding column 922. For example, the options 926 displayed inthe stats cell menu 924 include an option “Exclude from results” 934that a user can select to drill down and exclude the value 920 for thefield-value pair corresponding to the column 922. The stats cell menu924 also includes a designation 936 that corresponds to the selectableoption 934, and the designation 936 displays the field-value pair withthe respective value 920 that is listed in the corresponding column 922(e.g., “sourcetype=splunkd” in this example).

FIG. 9C illustrates an example of a statistics cell chart interface 938displayed as a graphical user interface in accordance with the disclosedembodiments for statistics chart interface cell mode drill down. Thestatistics cell chart interface 938 includes a search bar 940 thatdisplays a search command 942. The statistics cell chart interface 938displays rows 944, where each row is designated by a field value 946 ofan event field 948, which is the field “sourcetype” in this example. Thestatistics cell chart interface 938 also includes columns of fieldvalues 950 that are associated with one or more event fields, such asthe event fields “sourcetype” and “source” as indicated in the searchcommand 942 in this example. Each row in the statistics cell chartinterface 938 includes a field value 946 (e.g., of the event field 948)and one or more aggregated metrics 952, where each aggregated metricrepresents a number of events having the respective value 950 that islisted in the corresponding column for a designated row 944.

For example, a row 954 in the statistics cell chart interface 938 has afield value 956 of “splunkd”, and includes an aggregated metric “1303”shown at 958, indicating that 1,303 events have the value 956 “splunkd”and a value 960 of “/Users/cburke/Desktop . . . /splunk/splunkd.log”that is listed in the corresponding column 962 and in row 954. For agiven row and given column, the aggregated number is the count of eventsthat include the field-value pairs that are within the designated rowand column of the statistics cell chart interface 938. Inimplementations, the aggregated metrics 912 may be any type of metric,such as a count, an average, a sum, or any other aggregating metricassociated with a search result set of events.

In implementations, a cell 964 in the statistics cell chart interface938 may be emphasized (e.g., highlighted or any other type of visualemphasis) when a pointer that is displayed moves over a particular cell.This feature is also referred to as highlight with rollover (e.g., whena pointer moves over a cell). For example, a user may move a computermouse, stylus, or other input device pointer over the cell 964, which isthen displayed as an emphasized cell. The emphasized cell can then beselected in response to a user input, such as with a mouse click ortouch input to select a particular cell, such as shown and describedwith reference to FIG. 9D.

FIG. 9D further illustrates the example of the statistics cell chartinterface 938 described with reference to FIG. 9C in accordance with thedisclosed embodiments for statistics chart interface cell mode drilldown. In this example display of the statistics cell chart interface938, a user has selected the emphasized cell 964, such as with a mouseclick or touch input, which initiates display of a stats cell menu 924that is displayed responsive to the user input (e.g., similarlydisplayed in FIG. 9B). In implementations, the stats cell menu 924 isdisplayed proximate the emphasized cell 964 in the statistics cell chartinterface 938, such as a pop-up or drop-down menu just below theemphasized row.

The stats cell menu 924 includes options 926 that are selectable totransition to an events interface that displays a narrowed list ofevents that correspond to the field value 956 of the row 954 with theemphasized cell 964. For example, the options 926 displayed in the statscell menu 924 include an option “View events” 928 that a user can selectto transition to the events interface that displays the narrowed list ofthe events that include a field-value pairs with the respective value960 that is listed in the corresponding column 962 and respective value956 of the row 954 with the emphasized cell 964. The stats cell menu 924includes a designation 932 that corresponds to the selectable option928, the designation 932 indicating the respective field values 956 and960 for the field-value pairs.

The stats cell menu 924 also includes options 926 that are selectable todrill down into the table and initiate displaying the statistics cellchart interface 938 excluding the particular value 960 that is listed inthe corresponding column 962. For example, the options 926 displayed inthe stats cell menu 924 include an option “Exclude from results” 934that a user can select to drill down and exclude the value 960 for thefield-value pair corresponding to the column 962. The stats cell menu924 also includes a designation 936 that corresponds to the selectableoption 934, and the designation 936 displays the field-value pair withthe respective value 960 that is listed in the corresponding column 962.In cell mode, the statistics cell chart interface 938 is a split-bychart formulated based on the selected event fields (e.g., “source” and“sourcetype” in this example).

Example Methods

Example methods 1000 and 1100 are described with reference to respectiveFIGS. 10 and 11 in accordance with one or more embodiments of statisticstime chart interface cell mode drill down. Generally, any of thecomponents, modules, methods, and operations described herein can beimplemented using software, firmware, hardware (e.g., fixed logiccircuitry), manual processing, or any combination thereof. Someoperations of the example methods may be described in the generalcontext of executable instructions stored on computer-readable storagememory that is local and/or remote to a computer processing system, andimplementations can include software applications, programs, functions,and the like. Alternatively or in addition, any of the functionalitydescribed herein can be performed, at least in part, by one or morehardware logic components, such as, and without limitation,Field-programmable Gate Arrays (FPGAs), Application-specific IntegratedCircuits (ASICs), Application-specific Standard Products (ASSPs),System-on-a-chip systems (SoCs), Complex Programmable Logic Devices(CPLDs), and the like.

Computing devices (to include server devices) can be implemented withvarious components, such as a processing system and memory, and with anynumber and combination of different components as further described withreference to the example device shown in FIG. 12. One or more computingdevices can implement the search system, in hardware and at leastpartially in software, such as executable software instructions (e.g.,computer-executable instructions) that are executable with a processingsystem (e.g., one or more computer processors) implemented by the one ormore computing devices. The search system can be stored oncomputer-readable, non-volatile storage memory, such as any suitablememory device or electronic data storage implemented by the computingdevices.

FIG. 10 illustrates example method(s) 1000 of statistics value chartinterface cell mode drill down, and is generally described withreference to a statistics value chart interface. The method(s) 1000 maybe implemented by a computing device, a distributed system of computingdevices, and/or by one or more user client devices. The order in which amethod is described is not intended to be construed as a limitation, andany number or combination of the method operations and/or methods can beperformed in any order to implement a method, or an alternate method.

At 1002, a statistics value chart interface is displayed that includescolumns of field values associated with respective event fields, whereeach column includes a header as one of the event fields, and thestatistics value chart interface includes rows that each include thefield values of the respective event fields. For example, the statisticsvalue chart interface 800 (FIG. 8A) includes columns 806 of field values808 for designated event fields 810, where each column 806 includes aheader as one of the event fields 810. The statistics value chartinterface 800 also includes rows 812 that each include the field values808 of the respective event fields 810 in a particular row. Each of therows 812 in the statistics value chart interface 800 further include anaggregated metric 814 that identifies a number of events havingfield-value pairs corresponding to the one or more field values 808listed in a respective row 812.

At 1004, a cell in a row of the field values in the statistics valuechart interface is emphasized. For example, a cell 816 in a row 812 ofthe field values 808 in the statistics value chart interface 800 isemphasized responsive to detection of an input pointer over theemphasized cell. A user may move a computer mouse, stylus, or otherinput device pointer over the cell 816, which is then displayed as anemphasized cell.

At 1006, a selection is received of the emphasized cell in the row inthe statistics value chart interface and in response, at 1008, a statsevent menu is displayed with event options that are selectable totransition to an events interface or drill down into the statisticsvalue chart interface excluding the field value in the emphasized celland corresponding column. For example, a user selects the emphasizedcell 816, such as with a mouse click or touch input, which initiates adisplay of the stats event menu 818 (FIG. 8B) that is displayedresponsive to the user input and proximate the emphasized cell 816. Thestats event menu 818 includes the event options 820 displayed in thestats event menu as a view events option 822, an other events option824, an exclude from results option 828, and a new search option 830.

Further, the stats event menu 818 includes a designation 826 (FIG. 8B)of a field-value pair that includes the field value 808 of theemphasized cell 816. Alternatively, the stats event menu 818 includes adesignation 852 (FIGS. 8D and 8E) of a field-value pair that includesthe field value of the emphasized cell 850, and the field values 808listed in the row 812 to the left of the emphasized cell. Alternativelyor in addition, the stats event menu 818 includes a designation of afield-value pair that includes the field value of the emphasized cell,and one or more of the field values listed in the row of the emphasizedcell.

At 1010, a selection of the view events option is received to transitionto the events interface. For example, a user can select the option “Viewevents” 822 to transition to the events interface (FIG. 8C) thatdisplays a list of the events that include the field value 808 thatcorresponds to the column with the emphasized cell 816. At 1012, aselection of the other events option is received to transition to theevents interface. For example, a user can select the option “Otherevents” 824 that a user can select to transition to the events interfacethat displays a list of other events that do not include the field value808 that corresponds to the column with the emphasized cell 816. Theevents interface can display either a list of events that include thefield value of the emphasized cell, or other events that do not includethe field value of the emphasized cell. Alternatively, the eventsinterface displays a list of events that include one or more of thefield values listed in the row with the emphasized cell, or other eventsthat do not include one or more of the field values listed in the rowwith the emphasized cell. Alternatively or in addition, the eventsinterface displays a list of events that include the field value of theemphasized cell and the field values listed in the row to the left ofthe emphasized cell.

At 1014, a selection of the exclude from results option is received todrill down into the statistics value chart interface excluding the fieldvalue of the emphasized cell and the corresponding column. For example,a user can select the option “Exclude from results” 828 that a user canselect to drill down and exclude the field-value pair, which initiatesdisplaying the statistics value chart interface 800 excluding the fieldvalue in the emphasized cell 816. The option to drill down into thestatistics value chart interface excludes the field value in theemphasized cell and the corresponding column from the statistics valuechart interface.

At 1016, a selection of the new search option is received to create anew search based on the field value of the emphasized cell. For example,a user can select the option “New search” 830 that the user can selectto create a new search based on the field value in the emphasized cell816 (e.g., replacing the search command 804 in the search bar 802 withthe field value in the emphasized cell). A user selection of the newsearch option 830 from the stats event menu 818 can be received, and thesearch command 804 in the search bar 802 is updated based on the searchoption that is selected for the field value.

FIG. 11 illustrates example method(s) 1100 of statistics time chartinterface cell mode drill down, and is generally described withreference to a statistics time chart interface. The method(s) 1100 maybe implemented by a computing device, a distributed system of computingdevices, and/or by one or more user client devices. The order in which amethod is described is not intended to be construed as a limitation, andany number or combination of the method operations and/or methods can beperformed in any order to implement a method, or an alternate method.

At 1102, a statistics time chart interface is displayed that includesone or more columns of values associated with an event field, andincludes one or more rows, each including a time increment andaggregated metrics. For example, the statistics time chart interface 900(FIG. 9A) is displayed and includes the rows 906 that are eachdesignated by a time increment 908, and each time increment may includea date associated with the time increment. The statistics time chartinterface 900 also includes the columns of values 910 that areassociated with an event field. Each row in the statistics time chartinterface 900 includes a time increment 908 and one or more aggregatedmetrics 912, where each aggregated metric represents a number of theevents having the respective value 910 that is listed in thecorresponding column and within the time increment.

At 1104, a cell is emphasized in a row that includes the time incrementand the one or more aggregated metrics in the row of the statistics timechart interface. For example, a user may move a computer mouse, stylus,or other input device pointer over any of the one or more aggregatedmetrics 912 in a row 906, which then displays an emphasized cell (e.g.,highlighted or any other type of visual emphasis). The emphasized cellcan then be selected in response to a user input, such as with a mouseclick or touch input to select a particular row.

At 1106, a selection of an emphasized cell in the statistics time chartinterface is received and, at 1108, a stats cell menu is displayed withoptions that are selectable to transition to an events interface, ordrill down into the statistics time chart interface excluding therespective value and corresponding column. For example, the emphasizedcell 925 can be selected as a user input, such as with a mouse click ortouch input to select the emphasized row. The stats cell menu 924 (FIG.9B) is then displayed proximate the emphasized cell 925 in thestatistics time chart interface 900 based on the selection of theemphasized cell. The options displayed in the stats cell menu 924include the view events option 928 and the exclude from results option934. The stats cell menu 924 includes the designation 932 thatcorresponds to the view events option 928 and includes the time duration930 that encompasses the time increment 916 corresponding to the row 914with the emphasized cell 925, where the designation 932 further displaysthe field-value pair corresponding to the column 922 of the respectivevalue 920. The stats cell menu 924 also includes the designation 936that corresponds to the exclude from results option 934 and includes adisplay of the field-value pair corresponding to the column of therespective value.

At 1110, a selection of the view events option is received to transitionto the events interface. For example, a user can select the option “Viewevents” 928 to transition to the events interface that displays thenarrowed list of the events that include a field-value pair with therespective value 920 that is listed in the corresponding column 922 andwithin a time duration 930 of the time increment 916 of the row 914 withthe emphasized cell 925. At 1112, a selection of the exclude fromresults option is received to drill down into the statistics time chartinterface excluding the field-value pair corresponding to the column ofthe respective value. For example, a user can select the option “Excludefrom results” 934 to drill down and exclude the value 920 for thefield-value pair corresponding to the column 922.

Example System and Device

FIG. 12 illustrates an example system generally at 1200 that includes anexample computing device 1202 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe search interface module 1204 that is representative of functionalityto interact with a search service 1206, e.g., to specify and managesearches using a late-binding schema and events as described above andthus may correspond to the client application module 106 and system 100of FIG. 1. The computing device 1202 may be, for example, a server of aservice provider, a device associated with a client (e.g., a clientdevice), an on-chip system, and/or any other suitable computing deviceor computing system.

The example computing device 1202 as illustrated includes a processingsystem 1208, one or more computer-readable media 1210, and one or moreI/O interface 1212 that are communicatively coupled, one to another.Although not shown, the computing device 1202 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 1208 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1208 is illustrated as including hardware element 1214 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1214 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 1210 is illustrated as includingmemory/storage 1216. The memory/storage 1216 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1216 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1216 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 1210 may be configured in a variety of otherways as further described below.

Input/output interface(s) 1212 are representative of functionality toallow a user to enter commands and information to computing device 1202,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1202 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1202. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 1202, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1214 and computer-readablemedia 1210 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1214. The computing device 1202 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1202 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1214 of the processing system 1208. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1202 and/or processing systems1208) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1202 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1218 via a platform 1220 as describedbelow.

The cloud 1218 includes and/or is representative of a platform 1220 forresources 1222. The platform 1220 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1218. Theresources 1222 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1202. Resources 1222 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 1220 may abstract resources and functions to connect thecomputing device 1202 with other computing devices. The platform 1220may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resources1222 that are implemented via the platform 1220. Accordingly, in aninterconnected device embodiment, implementation of functionalitydescribed herein may be distributed throughout the system 1200. Forexample, the functionality may be implemented in part on the computingdevice 1202 as well as via the platform 1220 that abstracts thefunctionality of the cloud 1218.

Although embodiments of statistics time chart interface cell mode drilldown have been described in language specific to features and/ormethods, the appended claims are not necessarily limited to the specificfeatures or methods described. Rather, the specific features and methodsare disclosed as example implementations of statistics time chartinterface cell mode drill down, and other equivalent features andmethods are intended to be within the scope of the appended claims.Further, various different embodiments are described and it is to beappreciated that each described embodiment can be implementedindependently or in connection with one or more other describedembodiments.

1. A method, comprising: causing display of a first interface in a tableformat that includes: a first column and a second column, wherein thefirst column is associated with a plurality of time increments and thesecond column is associated with a first value of a first event field ofa plurality of events; a first cell, of the first column, that displaysa first time increment of the plurality of time increments; a secondcell, of the second column, that displays a first aggregated metric thatindicates a number of events of the plurality of events, wherein eachevent contributing to the number of events occurred within the firsttime increment and has a field-value pair that matches the first valueof the first event field; in response to receiving a first selection ofthe second cell and a second selection of a first option of a pluralityof options, identifying one or more events of the plurality of events,wherein each of the identified one or more events has a firstrelationship to the first time increment and a second relationship tothe first value of the first event field that is based on the selectedfirst option; and causing display of the identified one or more events.2. The method as recited in claim 1, wherein the first time incrementincludes a date.
 3. The method as recited in claim 1, wherein theidentified one or more events is displayed via a second interface thatdisplays a narrowed list of events, wherein each event included in thenarrowed lists of events has a field-value pair that matches the firstvalue of the first event field and occurred within the first timeincrement.
 4. The method as recited in claim 1, wherein the identifiedone or more events excludes events having a field-value pair matching adifferent value than the first value of the first event field.
 5. Themethod as recited in claim 1, wherein the first aggregated metricindicates a number of events corresponding to a first row of the firstinterface, and the identified one or more events includes each eventincluded in the number of events corresponding to the first row.
 6. Themethod as recited in claim 1, wherein the first relationship comprisesthe time being within the respective time increment.
 7. The method asrecited in claim 1, wherein the plurality of options are displayed in amenu of the first interface, and the menu includes a view events optioncorresponding to the first relationship comprising the identified one ormore events being within the first time increment and an exclude fromresults option corresponding to the first relationship comprising thetime being outside of the first time increment.
 8. The method as recitedin claim 1, wherein the plurality of options are displayed in a menu ofthe first interface, and the menu includes a view events option that isselectable to cause a second interface that displays the identified oneor more events to display a narrowed list of events, wherein each eventincluded in the narrowed list of events has a field-value pair thatmatches the first value of the first event field and occurred within thefirst time increment.
 9. The method as recited in claim 1, wherein theplurality of options are displayed in a menu of the second interface,and the menu includes an exclude from results option, wherein when theexclude from results option is selected, the first relationshipcomprises that the time associated with the identified one or moreevents is outside the first time increment, and the at least theidentified one or more events excludes events having a field-value pairmatching the first value of the first event field and occurred withinthe first time increment.
 10. The method as recited in claim 1, wherein:the plurality of options are displayed in a menu of the first interface,and the menu includes a view events option and an exclude from resultsoption, wherein when the view events option is selected, a secondinterface displays a narrowed list of events and each event included inthe narrowed list of events has a field-value pair that matches thefirst value of the first event field and occurred within the first timeincrement, and when the exclude from results option is selected, aseparate list of events is displayed by the second interface, theseparate list of events excludes the events having the field-value pairmatching the first value of the first event field.
 11. The method asrecited in claim 1, wherein a menu of the first interface displays theplurality of options, and the menu includes a view events option and acorresponding designation of a time duration that encompasses the firsttime increment, the corresponding designation further displaying afield-value pair matching the first value of the first event field 12.The method as recited in claim 1, wherein a menu of the first interfacedisplays the plurality of options, and the menu includes an exclude fromresults option and a corresponding designation displaying a field- valuepair matching a different value than the first value of the first eventfield.
 13. The method as recited in claim 1, wherein in response toreceiving the first selection of the second cell, the second cell isemphasized and is responsive to detection of an input pointer over thefirst aggregated metric of the second cell.
 14. The method as recited inclaim 1, wherein in response to receiving the first selection of thesecond cell, the second cell is emphasized by highlighting the secondcell.
 15. The method as recited in claim 1, wherein the identified oneor more events are initially derived from collected data that comprisesat least one of raw data, machine data, performance data, log data,diagnostic information, transformed data, or mashup data combined frommultiple sources.
 16. The method as recited in claim 1, wherein theidentified one or more events are returned as a result of a searchperformed using a late-binding schema on data originally collected fromone or more sources.
 17. The method as recited in claim 1, wherein eachevent of the identified one or more events comprises a portion of rawdata that is associated with a timestamp indicating a respective pointin time.
 18. A system, comprising: a processor device; and acomputer-readable storage medium, coupled with the processor device,having instructions stored thereon, which, when executed by theprocessor device, cause the system to perform actions comprising:causing display of a first interface in a table format that includes: afirst column and a second column, wherein the first column is associatedwith a plurality of time increments and the second column is associatedwith a first value of a first event field of a plurality of events; afirst cell, of the first column, that displays a first time increment ofthe plurality of time increments; a second cell, of the second column,that displays a first aggregated metric that indicates a number ofevents of the plurality of events, wherein each event contributing tothe number of events occurred within the first time increment and has afield-value pair that matches the first value of the first event field;in response to receiving a first selection of the second cell and asecond selection of a first option of a plurality of options,identifying one or more events of the plurality of events, wherein eachof the identified one or more events has a first relationship to thefirst time increment and a second relationship to the first value of thefirst event field that is based on the selected first option; andcausing display of the identified one or more events.
 19. The system asrecited in claim 18, wherein the first time increment includes a date.20. The system as recited in claim 18, wherein the identified one ormore events is displayed via a second interface that displays a narrowedlist of events, wherein each event included in the narrowed lists ofevents has a field-value pair that matches the first value of the firstevent field and occurred within the first time increment.
 21. The systemas recited in claim 18, wherein the identified one or more eventsexcludes events having a field-value pair matching a different valuethan the first value of the first event field.
 22. The system as recitedin claim 18, wherein the first aggregated metric indicates a number ofevents corresponding to a first row of the first interface, and theidentified one or more events includes each event included in the numberof events corresponding to the first row.
 23. The system as recited inclaim 18, wherein the first aggregated metric indicates a number ofevents corresponding to the first row.
 24. The system as recited inclaim 18, wherein the first relationship comprises the time being withinthe respective time increment.
 25. The system as recited in claim 18,wherein the plurality of options are displayed in a menu of the firstinterface, and the menu includes a view events option corresponding tothe first relationship comprising the identified one or more eventsbeing within the first time increment and an exclude from results optioncorresponding to the first relationship comprising the time beingoutside of the first time increment.
 26. One or more non-transitorycomputer-readable, non-volatile storage memory comprising storedinstructions that are executable and, responsive to execution by acomputing device, the computing device performs operations comprising:causing display of a first interface in a table format that includes: afirst column and a second column, wherein the first column is associatedwith a plurality of time increments and the second column is associatedwith a first value of a first event field of a plurality of events; afirst cell, of the first column, that displays a first time increment ofthe plurality of time increments; a second cell, of the second column,that displays a first aggregated metric that indicates a number ofevents of the plurality of events, wherein each event contributing tothe number of events occurred within the first time increment and has afield-value pair that matches the first value of the first event field;in response to receiving a first selection of the second cell and asecond selection of a first option of a plurality of options,identifying one or more events of the plurality of events, wherein eachof the identified one or more events has a first relationship to thefirst time increment and a second relationship to the first value of thefirst event field that is based on the selected first option; andcausing display of the identified one or more events.
 27. The one ormore computer-readable, non-volatile storage memory as recited in claim26, wherein the first time increment includes a date.
 28. The one ormore computer-readable, non-volatile storage memory as recited in claim26, wherein the identified one or more events is displayed via a secondinterface that displays a narrowed list of events, wherein each eventincluded in the narrowed lists of events has a field-value pair thatmatches the first value of the first event field and occurred within thefirst time increment.
 29. The one or more computer-readable,non-volatile storage memory as recited in claim 26, wherein theidentified one or more events excludes events having a field-value pairmatching a different value than the first value of the first eventfield.
 30. The one or more computer-readable, non-volatile storagememory as recited in claim 26, wherein the plurality of options aredisplayed in a menu of the first interface, and the menu includes a viewevents option that is selectable to cause a second interface thatdisplays the identified one or more events to display a narrowed list ofevents, wherein each event included in the narrowed list of events has afield-value pair that matches the first value of the first event fieldand occurred within the first time increment.